Table 1.

Statistical considerations in experimental design data collection for NO research in plants.

Section	Key considerations	Details/examples	References
2.1 Sampling stress for NO measurement	Random sampling	Ensures equal chance for each individual in the population to be selected	(Lohr, 2021)
	Stratified sampling	Divides population into subgroups based on characteristics (e.g., tissue type, developmental stage) for more accurate representation of NO data	(Singh and Chaudhary, 1981)
	Sample size determination	Power analysis to calculate minimum sample size required to detect significant effects with desired confidence	(Ryan, 2013)
2.2 Detection methods and their statistical implications	Chemiluminescence	Measures light emitted during the reaction of NO with ozone. Highly sensitive and specific for NO quantification	(Sparacino-Watkins and Lancaster, 2021)
	Fluorescence probes	Probes like DAF-FM and DAR-4M allow real-time NO measurements in living tissues, though can be influenced by environmental factors (e.g., pH, temperature)	(Goshi et al., 2019)
	Electron paramagnetic resonance	Used for direct measurement of NO and other free radicals, providing high sensitivity	(Calvo-Begueria et al., 2018)
	Calibration curves	Used to relate the measured signal (e.g., fluorescence intensity) to NO concentration	(Hetrick and Schoenfisch, 2009)
	Linear regression and R2	Linear regression analysis to generate calibration curves and assess the goodness of fit (R2 value) for accurate measurements	(Ebrahimzadeh et al., 2010)
	Limit of detection and limit of quantification	Establishes sensitivity and reliability of detection methods	(Hetrick and Schoenfisch, 2009)
2.3 Handling variability and noise in NO data	Control experiments	Use of NO scavengers or inhibitors to ensure specificity of NO measurements	(Astier et al., 2018)
	Replicates	Incorporating multiple measurements of the same condition to assess the consistency and reliability of data	(Arasimowicz-Jelonek et al., 2009)
	Coefficient of variance	Measures relative variability; low CV indicates high precession, while high CV suggests more variability that may need further investigation	(Canchola et al., 2017)
	Intraclass correlation	Evaluates reliability of repeated measurements or agreement between detection methods; higher ICC values indicate greater consistency and reliability	(Paciência et al., 2021)